Job description
StatNova Analytics is a leading data science consultancy delivering rigorous, data-driven insights to Fortune 500 clients. We are seeking an experienced Applied Statistics Analyst to join our Cambridge team and help translate complex data into clear, strategic actions.
In this role, you will design experiments, build predictive and causal models, and collaborate across product, marketing, and engineering teams to shape decision-making. If you are passionate about statistics, possess strong communication skills, and thrive in a fast-paced environment, we want to hear from you.
Responsibility
- Design and analyze experiments, A/B tests, and quasi-experiments to inform product and marketing decisions.
- Develop and validate predictive, causal, and segment-level models using R, Python, and SAS.
- Apply advanced statistical techniques (linear/nonlinear regression, time-series, survival analysis as relevant) to large datasets.
- Collaborate with product, data engineering, and business teams to translate insights into actionable recommendations and roadmaps.
- Automate analyses and create reproducible workflows using version control (Git) and documented methodologies.
- Prepare clear, concise data visualizations and executive summaries for non-technical stakeholders.
- Assess model assumptions, validate results, and document limitations and uncertainty.
- Monitor model performance and data quality; iterate and refine models over time.
Qualification
- Master's or PhD in Statistics, Mathematics, Data Science, or a closely related field; or 3+ years of relevant industry experience.
- Strong foundation in experimental design, hypothesis testing, regression, and time-series analysis.
- Proficiency in R and/or Python for statistical modeling; experience with SAS is a plus.
- SQL proficiency and experience querying large datasets; ability to optimize queries.
- Experience with data visualization tools (ggplot2, Matplotlib/Seaborn, Tableau) and communicating results to non-technical audiences.
- Knowledge of machine learning methods and causal inference is beneficial.
- Excellent written and verbal communication, collaboration, and problem-solving skills.
- Ability to manage multiple projects, prioritize effectively, and work in a fast-paced environment.